Currently, some approaches [1]}, [2]}, [3]}, [4]}, [5]}, [6]} have been proposed to alleviate data deviation. In detail, instead of only adopting a single sentence-level RNN to generate both the normal and abnormal sentences, [1]} introduced two RNNs as two different report writers, i.e., Normality Writer and Abnormality Writer, to help the model to generate more accurate normal and abnormal sentences, respectively. At the same time, [2]} proposed a hybrid model with template retrieval and text generation module, which focus on the generation of normal and abnormal sentences, respectively, to enhance the ability of model in describing abnormalities. Most recently, [4]} designed the medical graph based on prior knowledge from chest findings, in which each node is denoted by disease keywords representing one of the disease findings, so as to increase the capability of models to understand medical domain knowledge. Concurrently to our own work, the auxiliary signals introduced in [5]} is similar to the idea of our approach. In particular, [5]} only consider the medical graph, while we further leverage the disease topic tags and working experience to enhance the learning of posterior and prior knowledge, respectively. Besides, we further provides the evidence of this advantage of our approach on two public datasets.
